Comparison of the robust parameters estimation methods for the two-parameters Lomax distribution

Accurate and precise estimation of parameters in distribution theory is of immense significance. Imprecise and biased estimation of a probability distribution can lead to invalid and erroneous results. In this study, we investigate the Lomax distribution and introduced new four robust point estimation methods such as L-moments, trimmed L-moments, probability weighted moments, and generalized probability weighted moments (GPWM). We compare the efficiency of these methods with traditional method of moments based on performance measures such as bias, root-mean-square error and total deviation criteria using simulation study. We concluded that trimmed L-moments ascertained to be the superior method when the shape parameter is smaller (q < 3) and this assessment is equally valid for larger sample sizes, however, GPWM performs better for higher values of the shape parameter.